TestScripts/VAST_StratMean_Error_Comparison_All.R
In Blevy2/READ-PDB-blevy2-MFS2: Mixed Fishery fleet dynamics simulation tool
#THIS SCRIPT IS WAY TO GO BEYOND AIC AND MEASURE ACTUAL ERROR BETWEEN MODEL SELECTION OPTIONS AND
#TRUE MODEL OUTPUT
##################################################################################################
#THINGS WE NEED
##################################################################################################
scenario <- "ConPop_IncTemp_8_1"
#original project directory so we can switch back to it
orig.dir <- getwd()
n_spp <- 3
years_sim <- 22
years_cut <- 2
#survey results without noise
list_all_temp <- readRDS(paste("E:\\READ-PDB-blevy2-MFS2\\GB_Results\\",scenario,"\\list_all_",scenario,".RDS",sep=""))
#simulation results
memory.limit(45000)
result <- readRDS(paste("E:\\READ-PDB-blevy2-MFS2\\GB_Results\\",scenario,"\\result_",scenario,".RDS",sep=""))
#1- single set of random survey locations used in stratified mean analysis
surv_random <- readRDS(paste("E:\\READ-PDB-blevy2-MFS2\\GB_Results\\",scenario,"\\surv_random_",scenario,".RDS",sep=""))
#pick specific simulation
#For YT:
#for Conpop_ConTemp (also used for IncTemp) run 1 shows decent constant value with single spike early on
#for Incpop_ConTemp (also used for IncTemp) run 77 shows strong increase for yellowtail
#for DecPop_ConTemp run 25 shows clear decrease with small values towards the end
#for DecPop_IncTemp run 13 shows clear decrease with small values towards the end
#For Cod:
#for ConPop_ConTemp iteration 13 is pretty good
#for ConPop_IncTemp iteration 1 is pretty good
#for IncPop_ConTemp iteration 63 shows clear increase with some variation towards end
#for IncPop_IncTemp iteration 44 shows clear increase with some variation towards end
#for DecPop_ConTemp iteration 18 shows steady decline
#for DecPop_IncTemp iteration 44 shows steady decline
#For Haddock:
#for ConPop_ConTemp iteration 6 shows steady population with some small varability througout
#for ConPop_IncTemp iteration 3 shows steady population
#IncPop_ConTemp doesnt have great options but 98 pretty good
#IncPop_IncTemp 100 is pretty good
#DecPop_ConTemp 6 is pretty good
#DecPop_IncTemp 9 is pretty good
exclude_strata <- TRUE
covariates <- TRUE
ifelse(covariates==TRUE,{cov_dir <- paste("_with_",cov_used,sep="")},{cov_dir <- ""})
#choose which simulation iteration to use based on above
#good_iter <- c(1,13,6) #ConPop_ConTemp
good_iter <- c(1,1,3) #ConPop_IncTemp
#good_iter <- c(77,63,98) #IncPop_ConTemp
#good_iter <- c(77,44,100) #IncPop_IncTemp
#good_iter <- c(25,18,6) #DecPop_ConTemp
#good_iter <- c(13,44,9) #DecPop_IncTemp
list_all <- list()
list_all[["YT"]] <- list_all_temp[[good_iter[1]]]
list_all[["Cod"]] <- list_all_temp[[good_iter[2]]]
list_all[["Had"]] <- list_all_temp[[good_iter[3]]]
###############################
#spp1 spp2 spp3
short_names <- c("YT","Cod","Had") #fixed above
#strata that each species occupies. Used to calculate stratified random mean of each
strata_species <- list()
strata_species[["YT"]] <- c(13,14,15,16,17,18,19,20,21)
strata_species[["Cod"]] <- c(13,14,15,16,17,18,19,20,21,22,23,24,25)
strata_species[["Had"]] <- c(13,14,15,16,17,18,19,20,21,22,23,24,25,29,30)
##################################################################################################
setwd(orig.dir)
#ADD TRUE MODEL POPULATION VALUES TO SURVEY DATA TABLES
#ALSO ADD LAT LON LOCATIONS TO TABLE AS WELL
library(raster)
library(sp)
library(TMB)
library(VAST)
library(dplyr)
library(ggplot2)
#read in habitat matrix
hab <- readRDS(file="hab_GB_3species.RDS") #courser resolution
#read in GB strata
#haddock contains all stratas used
Had_ras <- readRDS(file="TestScripts/Habitat_plots/Haddock/Had_Weighted_AdaptFalse_RASTER_res2.RDS")
#plot(Had_ras)
#translate habitat matrix back into raster
hab_ras <-raster(hab$hab$spp3)
extent(hab_ras) <- extent(Had_ras)
#plot(hab_ras)
for(iter in seq(length(list_all))){
print(iter)
temp <- matrix(data=0,nrow=length(list_all[[iter]][,1]),ncol=n_spp)
lat <- vector()
lon <- vector()
for(samp in seq(length(list_all[[iter]][,1]))){
#ADDING TRUE POPULATION
x = as.numeric(list_all[[iter]][samp,2]) #x in second column
y = as.numeric(list_all[[iter]][samp,3]) #y in third column
wk = as.numeric(list_all[[iter]][samp,11]) #week in 11th column
yr = as.numeric(list_all[[iter]][samp,7]) #year in 7th column
temp[samp,1] <- sum(result[[good_iter[iter]]]$pop_bios[[(wk+(52*(yr-1)))]][["spp1"]],na.rm=T) #YT is spp1
temp[samp,2] <- sum(result[[good_iter[iter]]]$pop_bios[[(wk+(52*(yr-1)))]][["spp2"]],na.rm=T) #Cod is spp2
temp[samp,3] <- sum(result[[good_iter[iter]]]$pop_bios[[(wk+(52*(yr-1)))]][["spp3"]],na.rm=T) #Had is spp3
#ADDING LAT LON LOCATIONS
rw <- as.numeric(list_all[[iter]][samp,"x"]) #x in col 2
cl <- as.numeric(list_all[[iter]][samp,"y"]) #y in col 3
lon[samp] <- xFromCol(hab_ras, col = cl)
lat[samp] <- yFromRow(hab_ras, row = rw)
}
temp <- cbind(temp,lat,lon)
colnames(temp) <- c("YT","Cod","Had","Latitude","Longitude")
list_all[[iter]] <- cbind(list_all[[iter]],temp)
colnames(list_all[[iter]]) <- c("station_no","x","y","stratum","day","tow","year","YT_samp","Cod_samp","Had_samp","week","Season","YT_pop","Cod_pop","Had_pop","Lat","Lon")
}
#SAVE INDIVIDUAL LIST_ALL AS THEY COME OUT SO DONT HAVE TO REDO THEM
#saveRDS(list_all,paste("list_all_more_",scenario,".RDS",sep=""))
#FIND MEAN VALUE BY SEASON USING ABOVE INFORMATION. USE MEAN OF TWO SURVEY WEEKS FOR EACH SEASON
season_wks <- list(c(13,14),c(37,38))
pop_by_season <- list()
for(iter in seq(length(list_all))){
for(s in short_names){
temp <- data.frame()
idx <- 1
for(yr in seq(3,22)){
for(season in seq(2)){
temp[idx,1] <- yr
temp[idx,2] <- season
#use values in given year for weeks in specified season. only use single strata because entire population summarized in each strata in above loop
temp[idx,3] <- mean(as.numeric(list_all[[iter]][((as.numeric(list_all[[iter]][,"year"]==yr)) & (as.numeric(list_all[[iter]][,"week"]) %in% season_wks[[season]]) & (as.numeric(list_all[[iter]][,"stratum"]==29)) ),paste(s,"_pop",sep="")]))
idx <- idx + 1
}
}
colnames(temp) <- c("year","season","biomass")
pop_by_season[[s]][[iter]] <- temp
}
}
pop_by_season[["YT"]] <- pop_by_season[["YT"]][[1]] #YT should be first in list_all
pop_by_season[["Cod"]] <- pop_by_season[["Cod"]][[2]] #Cod should be second in list_all
pop_by_season[["Had"]] <- pop_by_season[["Had"]][[3]] #Had should be third in list_all
##########################################################################################
#NOW WE NEED TO CREATE A STRATIFIED MEAN FROM EACH OF THESE SAMPLES
##########################################################################################
#choose some strata to exclude, if desired
#George's Bank Setup by species
#YT Cod Haddock
exclude <- list()
ifelse(exclude_strata==TRUE,
{exclude[["YT"]] <- c(13,14,15,17,18)
exclude[["Cod"]] <- c(23,24,25)
exclude[["Had"]] <- c(23,24,25,29,30)},
{exclude[["YT"]] <- c(0)
exclude[["Cod"]] <- c(0)
exclude[["Had"]] <- c(0)})
setwd(orig.dir)
#BELOW WILL TAKE A MINUTE
#there are #strata * #iterations * #samp_per_iter total samples
#I AM COPYING FROM CALC_SRS_INDEX_SURVEY_BENS, which was adapted from Liz's code to create below
library(tibble)
library(ggplot2)
library(plyr)
library(dplyr)
library(tidyr)
library(readr)
library(here)
#stop output from below using this option
options(dplyr.summarise.inform = FALSE)
#load file to calculate the stratified mean
source("TestScripts/Calc_strat_mean/fn_srs_survey_BENS.R")
#setup dimensions for each species- 1 for each strata
strat_mean_all <- vector("list",length(seq(n_spp)))
for(s in seq(n_spp)){
strat_mean_all[[s]] <- vector("list",length(list_all))
}
#go through each strata survey, iteration, sample
for(iter in seq(length(list_all))){
print(iter)
#if any NA columns make them zero
list_all[[iter]][is.na(list_all[[iter]])]=0
# calculate SRS estimates ====
for(s in seq(n_spp)){
#DEFINE INDIVIDUAL STRATUM AREAS
stratum <- sort(unique(surv_random$log.mat[,4]))
STRATUM_AREA <- na.omit(surv_random$cells_per_strata) # old way: rep(10000/nstrata,nstrata) #100x100 grid so each corner has area 2500
sv.area <- as_tibble(data.frame(stratum,STRATUM_AREA))
#remove stratum that species does not occupy
sv.area <- sv.area[(sv.area$stratum %in% strata_species[[short_names[s]]]),]#sv.area %>% slice(-exclude)
#remove strata to exclude from stratified mean calculation
sv.area <- sv.area[!(sv.area$stratum %in% exclude[[s]]),]#sv.area %>% slice(-exclude)
spp <- as_tibble(list_all[[iter]],header=T) #pull out entire survey matrix
##remove strata to exclude from stratified mean calculation
spp <- spp[(spp$stratum %in% strata_species[[short_names[s]]]),]
spp <- spp[!(spp$stratum %in% exclude[[s]]),]
spp$year <- as.numeric(spp$year)
# get total area of stock ====
spp.strata <- unique(spp$stratum)
spp.strata <- as.numeric(spp.strata)
spp.area <- sum(sv.area$STRATUM_AREA[sv.area$stratum %in% spp.strata]) #TOTAL AREA OF ALL STRATA
temp <- srs_survey(df=spp, sa=sv.area, str=NULL, ta=1, sppname = paste0(short_names[s],"_samp", sep="") ) # if strata=NULL, the function will use the unique strata set found in df
# View(temp)
strat_mean_all[[s]][[iter]] <- temp %>%
mutate(mean.yr.absolute=mean.yr*spp.area, sd.mean.yr.absolute=sd.mean.yr*spp.area,
CV.absolute=sd.mean.yr.absolute/mean.yr.absolute) # if strata=NULL, the function will use the unique strata set found in df
strat_mean_all[[s]][[iter]] <- data.matrix(strat_mean_all[[s]][[iter]])
}
colnames(strat_mean_all[[s]][[iter]]) <- c("year","mean.yr","var.mean.yr","sd.mean.yr","CV","season","mean.yr.absolute","sd.mean.yr.absolute","CV.absolute")
}
#initial scenario folder
dir.create( paste0(getwd(),"/VAST/",scenario)) #create folder to store upcoming subfolders
strat_mean_all[["YT"]] <- strat_mean_all[[1]][[1]] #YT should be first in list_all
strat_mean_all[["Cod"]] <- strat_mean_all[[2]][[2]] #Cod should be second in list_all
strat_mean_all[["Had"]] <- strat_mean_all[[3]][[3]] #Had should be third in list_all
ifelse(exclude_strata==TRUE, strat_ex <- "excludestrata", strat_ex <- "allstrata")
saveRDS(strat_mean_all,paste0(getwd(),"/VAST/",scenario,"/strat_mean_all_",scenario,"_",strat_ex,".RDS"))
#load VAST fit index approximation, measure error with true value, store
#individual strata limits
# strata.limits <- list()
# strata.limits[["YT"]] <- data.frame(Georges_Bank = c(1130, 1140, 1150, 1160, 1170, 1180, 1190, 1200, 1210)) #THESE ARE YTF STRATA
# strata.limits[["Cod"]] <- data.frame(Georges_Bank = c(1130, 1140, 1150, 1160, 1170, 1180, 1190, 1200, 1210, 1220, 1230, 1240, 1250)) #THESE ARE COD STRATA
# strata.limits[["Had"]] <- data.frame(Georges_Bank = c(1130, 1140, 1150, 1160, 1170, 1180, 1190, 1200, 1210, 1220, 1230, 1240, 1250, 1290, 1300)) #THESE ARE HAD STRATA
#What I tested for all of them, but they dont always work in each scenario
model_types <- list()
model_types[["YT"]] <- c("obsmodel5","obsmodel6")
model_types[["Cod"]] <- c("obsmodel1","obsmodel2","obsmodel5","obsmodel6")
model_types[["Had"]] <- c("obsmodel1","obsmodel2","obsmodel5","obsmodel6")
#Use these for IncPop_IncTemp
# model_types <- list()
# model_types[["YT"]] <- c("obsmodel5","obsmodel6")
# model_types[["Cod"]] <- c("obsmodel2","obsmodel5") #,"obsmodel6" didnt work
# model_types[["Had"]] <- c("obsmodel1")
# ifelse(exclude_strata==TRUE,
# {model_types[["YT"]] <- c("obsmodel5","obsmodel6")
# model_types[["Cod"]] <- c("obsmodel1","obsmodel2","obsmodel5") #,"obsmodel6" didnt work
# model_types[["Had"]] <- c("obsmodel1","obsmodel2","obsmodel5","obsmodel6")},
# {model_types[["YT"]] <- c("obsmodel1","obsmodel2","obsmodel3","obsmodel4","obsmodel5","obsmodel6")
# model_types[["Cod"]] <- c("obsmodel1","obsmodel2","obsmodel3","obsmodel4","obsmodel5","obsmodel6")
# model_types[["Had"]] <- c("obsmodel1","obsmodel2","obsmodel3","obsmodel4","obsmodel5","obsmodel6")}
# )
Model_settings <- list()
Model_AIC <- list()
SRS_data_all <- list()
SRS_data1 <- list()
VAST_Model_error <- list()
SRS_Model_error <- list()
VAST_fit <- list()
VAST_est <- list()
FC_settings <- list()
FC_settings[["YT"]][["spring"]] <- data.frame(row.names = model_types[["YT"]])
FC_settings[["YT"]][["fall"]] <- data.frame(row.names = model_types[["YT"]])
FC_settings[["Cod"]][["spring"]] <- data.frame(row.names = model_types[["Cod"]])
FC_settings[["Cod"]][["fall"]] <- data.frame(row.names = model_types[["Cod"]])
FC_settings[["Had"]][["spring"]] <- data.frame(row.names = model_types[["Had"]])
FC_settings[["Had"]][["fall"]] <- data.frame(row.names = model_types[["Had"]])
#for getting into correct subfolder
ifelse(exclude_strata==TRUE,
{str_dir <- "ExcludeStrata"},
{str_dir <- "AllStrata"})
#initial scenario folder
setwd( paste0(orig.dir,"/VAST/",scenario,sep="")) #create folder to store upcoming subfolders
for(s in short_names){
for(folder in model_types[[s]]){
print(folder)
for(sn in c("fall","spring")){
try(fit <- readRDS(paste0(getwd(),"/",s,"/",str_dir,cov_dir,"/",folder,"/",sn,"/fit_",sn,".RDS")),silent=TRUE)
#set FC settings as N and then override them, if possible
FC_settings[[s]][[sn]][folder,1] <- "N"
FC_settings[[s]][[sn]][folder,2] <- "N"
FC_settings[[s]][[sn]][folder,3] <- "N"
FC_settings[[s]][[sn]][folder,4] <- "N"
try(FC_settings[[s]][[sn]][folder,1] <- fit$settings$FieldConfig[[1]],silent=TRUE)
try( FC_settings[[s]][[sn]][folder,2] <- fit$settings$FieldConfig[[2]],silent=TRUE)
try( FC_settings[[s]][[sn]][folder,3] <- fit$settings$FieldConfig[[3]],silent=TRUE)
try(FC_settings[[s]][[sn]][folder,4] <- fit$settings$FieldConfig[[4]],silent=TRUE)
try(VAST_fit[[s]][[folder]][[sn]] <- read.csv(paste0(getwd(),"/",s,"/",str_dir,cov_dir,"/",folder,"/",sn,"/Index.csv"), header=T),silent=TRUE)
#pull out strat mean calc
SRS_data_all[[s]][[folder]][["spring"]] <- strat_mean_all[[s]][strat_mean_all[[s]][,"season"]==1,]
SRS_data_all[[s]][[folder]][["fall"]] <- strat_mean_all[[s]][strat_mean_all[[s]][,"season"]==2,]
ifelse(sn == "spring",
#add year & season to these
{Year <- seq(years_cut+1,years_sim)
season <- rep(1,years_sim-years_cut)},
{Year <- seq(years_cut+1,years_sim)
season <- rep(2,years_sim-years_cut)})
try(VAST_est[[s]][[folder]][[sn]] <- cbind(VAST_fit[[s]][[folder]][[sn]],Year,season),silent=TRUE)
SRS_data1[[s]][[folder]][[sn]] <- SRS_data_all[[s]][[folder]][[sn]][,c("mean.yr.absolute","year","season","sd.mean.yr.absolute")]
try(Model_AIC[[s]][[sn]][[folder]] <- fit$parameter_estimates$AIC,silent=TRUE)
try(Model_settings[[s]][[sn]][[folder]] <- read.delim(paste0(getwd(),"/",s,"/",str_dir,cov_dir,"/",folder,"/",sn,"/settings.txt",sep="")),silent=TRUE)
try(remove(fit),silent=TRUE)
}
}
colnames(FC_settings[[s]][["spring"]]) <- c("Omega1","Epsilon1","Omega2","Epsilon2")
colnames(FC_settings[[s]][["fall"]]) <- c("Omega1","Epsilon1","Omega2","Epsilon2")
}
pdf(file=paste(getwd(),"/",scenario,"_ErrorPlot_",str_dir,cov_dir,".pdf",sep=""))
year_min <- 2 #in case you dont want to plot all of the years
VAST_data <- list()
SRS_data <- list()
Obsmodel_plot <- list()
#plot stratified calculation and population estimate on same plot
#first make model output have 2 seasons to match the stratified mean calcs
for(s in short_names){ #LIST_ALL WILL BE LENGTH 3 FROM ABOVE
for(folder in model_types[[s]]){
#MODEL VALUES
model_spring = pop_by_season[[s]][pop_by_season[[s]]$season==1,"biomass"]
model_fall = pop_by_season[[s]][pop_by_season[[s]]$season==2,"biomass"]
# 2NORM
# #calculate SPRING VAST error from each iteration
# VAST_Model_error[[s]][[folder]][["spring"]] <- norm(model_spring- VAST_est[[s]][[folder]][["spring"]][,"Estimate"] , type="2") / norm(model_spring , type ="2")
#
# #calculate FALL VAST error from each iteration
# VAST_Model_error[[s]][[folder]][["fall"]] <- norm(model_fall- VAST_est[[s]][[folder]][["fall"]][,"Estimate"] , type="2") / norm(model_fall , type ="2")
print(s)
print(folder)
#ABSOLUTE SUM
#calculate SPRING VAST error from each iteration
#set as 99 and then override if possible
VAST_Model_error[[s]][[folder]][["spring"]] <- 99
try(VAST_Model_error[[s]][[folder]][["spring"]] <- sum(abs(model_spring- VAST_est[[s]][[folder]][["spring"]][,"Estimate"] )) / sum(abs(model_spring )),silent=TRUE)
VAST_Model_error[[s]][[folder]][["fall"]] <- 99
#calculate FALL VAST error from each iteration
try(VAST_Model_error[[s]][[folder]][["fall"]] <- sum(abs(model_fall- VAST_est[[s]][[folder]][["fall"]][,"Estimate"] )) / sum(abs(model_fall )),silent=TRUE)
#calculate SPRING SRS error from each iteration
SRS_Model_error[[s]][[folder]][["spring"]] <- sum(abs(model_spring- SRS_data1[[s]][[folder]][["spring"]][,"mean.yr.absolute"] )) / sum(abs(model_spring ))
#calculate FALL SRS error from each iteration
SRS_Model_error[[s]][[folder]][["fall"]] <- sum(abs(model_fall- SRS_data1[[s]][[folder]][["fall"]][,"mean.yr.absolute"] )) / sum(abs(model_fall ))
#store VAST stuff to plot later
#first load a blank version and override if possible
VAST_data[[s]][[folder]] <- readRDS(file = paste0(orig.dir,"/VAST/zero_VAST_est.RDS",sep="") )
#for decpop_contemp
#if(((!((s=="Cod"&folder=="obsmodel6")|(s=="Had"&folder=="obsmodel6"))))){ try(VAST_data[[s]][[folder]] <- rbind(VAST_est[[s]][[folder]][["spring"]],VAST_est[[s]][[folder]][["fall"]]),silent=TRUE)}
if(length(VAST_est[[s]][[folder]][["fall"]][1,])>2){try(VAST_data[[s]][[folder]] <- rbind(VAST_est[[s]][[folder]][["spring"]],VAST_est[[s]][[folder]][["fall"]]),silent=TRUE)}
SRS_data[[s]][[folder]] <- rbind(SRS_data1[[s]][[folder]][["spring"]],SRS_data1[[s]][[folder]][["fall"]])
long_names <- c("Yellowtail Flounder", "Atlantic Cod", "Haddock")
#NEW WAY PLOTTING 3 TOGETHER ON SAME PAGE
#field config settings for plotting
FC_fall = c(FC_settings[[s]]$fall[folder,1],FC_settings[[s]]$fall[folder,2],FC_settings[[s]]$fall[folder,3],FC_settings[[s]]$fall[folder,4])
FC_spring = c(FC_settings[[s]]$spring[folder,1],FC_settings[[s]]$spring[folder,2],FC_settings[[s]]$spring[folder,3],FC_settings[[s]]$spring[folder,4])
#store each obsmodel plot
Obsmodel_plot[[s]][[folder]] <- ggplot() +
#this way plots data by season
geom_point(data = subset(as.data.frame(pop_by_season[[s]]),year>=year_min), aes(x=as.numeric(year),y=biomass, group = season, color = "Model"),size=2) +
geom_line(data = subset(as.data.frame(pop_by_season[[s]]),year>=year_min), aes(x=as.numeric(year),y=biomass, group =season, color = "Model"),size=1) +
#plot VAST estimate
geom_errorbar(data=subset(VAST_data[[s]][[folder]],Year>=year_min),aes(x=Year,y=Estimate,group=season,ymin=Estimate-(1.96*Std..Error.for.Estimate), ymax=Estimate+(1.96*Std..Error.for.Estimate), color = "VAST Estimate"),width=.3) +
#geom_linerange(data=subset(VAST_data[[s]][[folder]],Year>=year_min),aes(x=Year,y=Estimate,group=season,ymin=Estimate-(1.96*Std..Error.for.Estimate), ymax=Estimate+(1.96*Std..Error.for.Estimate), color = "VAST Estimate")) +
geom_point(data=subset(VAST_data[[s]][[folder]],Year>=year_min),aes(x=Year,y=Estimate,group=season, color = "VAST Estimate"))+
geom_line(data=subset(VAST_data[[s]][[folder]],Year>=year_min),aes(x=Year,y=Estimate,group=season, color = "VAST Estimate"))+
#plot stratified calculation data
geom_errorbar(data=as.data.frame(SRS_data[[s]][[folder]]),aes(x=year,y=mean.yr.absolute,group=season,ymin=mean.yr.absolute-(1.96*sd.mean.yr.absolute), ymax=mean.yr.absolute+(1.96*sd.mean.yr.absolute), color = "Stratified Mean"),width=.3) +
# geom_linerange(data=as.data.frame(SRS_data[[s]][[folder]]),aes(x=year,y=mean.yr.absolute,group=season,ymin=mean.yr.absolute-(1.96*sd.mean.yr.absolute), ymax=mean.yr.absolute+(1.96*sd.mean.yr.absolute), color = "Stratified Mean")) +
geom_point(data=as.data.frame(SRS_data[[s]][[folder]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
geom_line(data=as.data.frame(SRS_data[[s]][[folder]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
facet_wrap(~ season, ncol =1) +
# labs(x="year",y="Biomass", title = paste(folder," SeV=",round(VAST_Model_error[[s]][[folder]][["spring"]],digits=2),
# " FC=", toString(FC_spring),
# " SeSM=",round(SRS_Model_error[[s]][[folder]][["spring"]],digits=2),
# " FeV=",round(VAST_Model_error[[s]][[folder]][["fall"]],digits=2),
# " FC=", toString(FC_fall),
# " FeSM=",round(SRS_Model_error[[s]][[folder]][["fall"]],digits=2),sep=""), color ="" )
labs(x="year",y="Biomass", title = paste(s," ",folder," SeV=",round(VAST_Model_error[[s]][[folder]][["spring"]],digits=2),
" SeSM=",round(SRS_Model_error[[s]][[folder]][["spring"]],digits=2),
" FeV=",round(VAST_Model_error[[s]][[folder]][["fall"]],digits=2),
" FeSM=",round(SRS_Model_error[[s]][[folder]][["fall"]],digits=2),sep=""), color ="" )
#one plot per page
print(Obsmodel_plot[[s]][[folder]])
# for more than one plot per page
# gridExtra::grid.arrange(Obsmodel_plot[[1]],Obsmodel_plot[[2]],Obsmodel_plot[[3]],nrow=3)
# gridExtra::grid.arrange(Obsmodel_plot[[4]],Obsmodel_plot[[5]],Obsmodel_plot[[6]],nrow=3)
}
}
dev.off()
BELOW HAS NOT YET BEEN UPDATED TO BE GENERALIZED FOR ALL SPECIES. ONLY APPLIES TO YTF
##########################################################################################
#Next plot scatterplot of errors
##########################################################################################
#first create data from for each
#1- stratified mean data
df_SRS_spring <- tibble(iter = rep(1:length(list_all),n_spp),
error = c(SRS_error_spring[[1]], SRS_error_spring[[2]], SRS_error_spring[[3]]),
species = c(rep("YTF",length(list_all)),rep("Cod",length(list_all)),rep("Had",length(list_all))),
season = rep(rep("spring",length(list_all)),n_spp),
Model = rep(rep("Strat. Mean",length(list_all)),n_spp),
)
df_SRS_fall <- tibble(iter = rep(1:length(list_all),n_spp),
error = c(SRS_error_fall[[1]], SRS_error_fall[[2]], SRS_error_fall[[3]]),
species = c(rep("YTF",length(list_all)),rep("Cod",length(list_all)),rep("Had",length(list_all))),
season = rep(rep("fall",length(list_all)),n_spp),
Model = rep(rep("Strat. Mean",length(list_all)),n_spp),
)
df_SRS <- rbind(as.data.frame(df_SRS_fall),as.data.frame(df_SRS_spring))
#create data frame containing mean values for each group
means_SRS <- ddply(df_SRS, .(species,season), summarise, mean = mean(as.numeric(unlist(error)),na.rm=T), Model = "Strat. Mean")
#2- VAST data
df_VAST_spring <- tibble(iter = rep(1:length(list_all),n_spp),
error = c(VAST_error_spring[[1]], VAST_error_spring[[2]], VAST_error_spring[[3]]),
species = c(rep("YTF",length(list_all)),rep("Cod",length(list_all)),rep("Had",length(list_all))),
season = rep(rep("spring",length(list_all)),n_spp),
Model = rep(rep("VAST",length(list_all)),n_spp),
)
df_VAST_fall <- tibble(iter = rep(1:length(list_all),n_spp),
error = c(VAST_error_fall[[1]], VAST_error_fall[[2]], VAST_error_fall[[3]]),
species = c(rep("YTF",length(list_all)),rep("Cod",length(list_all)),rep("Had",length(list_all))),
season = rep(rep("fall",length(list_all)),n_spp),
Model = rep(rep("VAST",length(list_all)),n_spp),
)
df_VAST <- rbind(df_VAST_fall,df_VAST_spring)
#create data frame containing mean values for each group
means_VAST <- ddply(df_VAST, .(species,season), summarise, mean = mean(as.numeric(unlist(error))), Model = "VAST")
#combine both of previous data into single object for plotting
df <- rbind(df_VAST,df_SRS)
means <- rbind(means_SRS,means_VAST)
#Error scatterplots
#1) to plot a single scenario, run just cc below and print(cc)
#2) to plot two scenarios on top of each other, store the first as cc and then run code below doing cc +
library(ggplot2)
# #SRS scatterplot alone
# SRS_scat <-ggplot(data=df_SRS,
# aes(x=iter,y=as.numeric(error),color=Model)) +
# geom_point()+
# ylim(0,1)+
# facet_grid(season ~ species)+
# geom_hline(aes(yintercept = mean, color = Model), data = means_SRS)
#
# print(SRS_scat)
#
# #VAST scatterplot alone
# VAST_scat <-ggplot(data=df_VAST,
# aes(x=iter,y=as.numeric(error),color=Model)) +
# geom_point()+
# ylim(0,1)+
# facet_grid(season ~ species)+
# geom_hline(aes(yintercept = mean, color = Model), data = means_VAST)
#
# print(VAST_scat)
#both scatterplots together
both_scat <-ggplot(data=df,
aes(x=iter,y=as.numeric(unlist(error)),color=Model)) +
geom_point()+
ylim(0,1)+
facet_grid(season ~ species)+
geom_hline(aes(yintercept = mean, color = Model), data = means)
print(both_scat)
#
# ggsave(filename = paste("Results/GB_error_plots/Individussssal_SRS_",scenario,".pdf",sep=""),
# plot = last_plot())
#
#run this second to plot on top of each other
cc+ geom_point(data=df,color="red",
aes(x=iter,y=as.numeric(error)))+
facet_grid(season ~ species) +
geom_hline(aes(yintercept = mean), data = means, color = "red")
Blevy2/READ-PDB-blevy2-MFS2 documentation built on Nov. 29, 2023, 11:48 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#THIS SCRIPT IS WAY TO GO BEYOND AIC AND MEASURE ACTUAL ERROR BETWEEN MODEL SELECTION OPTIONS AND
#TRUE MODEL OUTPUT
##################################################################################################
#THINGS WE NEED
##################################################################################################
scenario <- "ConPop_IncTemp_8_1"
#original project directory so we can switch back to it
orig.dir <- getwd()
n_spp <- 3
years_sim <- 22
years_cut <- 2
#survey results without noise
list_all_temp <- readRDS(paste("E:\\READ-PDB-blevy2-MFS2\\GB_Results\\",scenario,"\\list_all_",scenario,".RDS",sep=""))
#simulation results
memory.limit(45000)
result <- readRDS(paste("E:\\READ-PDB-blevy2-MFS2\\GB_Results\\",scenario,"\\result_",scenario,".RDS",sep=""))
#1- single set of random survey locations used in stratified mean analysis
surv_random <- readRDS(paste("E:\\READ-PDB-blevy2-MFS2\\GB_Results\\",scenario,"\\surv_random_",scenario,".RDS",sep=""))
#pick specific simulation
#For YT:
#for Conpop_ConTemp (also used for IncTemp) run 1 shows decent constant value with single spike early on
#for Incpop_ConTemp (also used for IncTemp) run 77 shows strong increase for yellowtail
#for DecPop_ConTemp run 25 shows clear decrease with small values towards the end
#for DecPop_IncTemp run 13 shows clear decrease with small values towards the end
#For Cod:
#for ConPop_ConTemp iteration 13 is pretty good
#for ConPop_IncTemp iteration 1 is pretty good
#for IncPop_ConTemp iteration 63 shows clear increase with some variation towards end
#for IncPop_IncTemp iteration 44 shows clear increase with some variation towards end
#for DecPop_ConTemp iteration 18 shows steady decline
#for DecPop_IncTemp iteration 44 shows steady decline
#For Haddock:
#for ConPop_ConTemp iteration 6 shows steady population with some small varability througout
#for ConPop_IncTemp iteration 3 shows steady population
#IncPop_ConTemp doesnt have great options but 98 pretty good
#IncPop_IncTemp 100 is pretty good
#DecPop_ConTemp 6 is pretty good
#DecPop_IncTemp 9 is pretty good
exclude_strata <- TRUE
covariates <- TRUE
ifelse(covariates==TRUE,{cov_dir <- paste("_with_",cov_used,sep="")},{cov_dir <- ""})
#choose which simulation iteration to use based on above
#good_iter <- c(1,13,6) #ConPop_ConTemp
good_iter <- c(1,1,3) #ConPop_IncTemp
#good_iter <- c(77,63,98) #IncPop_ConTemp
#good_iter <- c(77,44,100) #IncPop_IncTemp
#good_iter <- c(25,18,6) #DecPop_ConTemp
#good_iter <- c(13,44,9) #DecPop_IncTemp
list_all <- list()
list_all[["YT"]] <- list_all_temp[[good_iter[1]]]
list_all[["Cod"]] <- list_all_temp[[good_iter[2]]]
list_all[["Had"]] <- list_all_temp[[good_iter[3]]]
###############################
#spp1 spp2 spp3
short_names <- c("YT","Cod","Had") #fixed above
#strata that each species occupies. Used to calculate stratified random mean of each
strata_species <- list()
strata_species[["YT"]] <- c(13,14,15,16,17,18,19,20,21)
strata_species[["Cod"]] <- c(13,14,15,16,17,18,19,20,21,22,23,24,25)
strata_species[["Had"]] <- c(13,14,15,16,17,18,19,20,21,22,23,24,25,29,30)
##################################################################################################
setwd(orig.dir)
#ADD TRUE MODEL POPULATION VALUES TO SURVEY DATA TABLES
#ALSO ADD LAT LON LOCATIONS TO TABLE AS WELL
library(raster)
library(sp)
library(TMB)
library(VAST)
library(dplyr)
library(ggplot2)
#read in habitat matrix
hab <- readRDS(file="hab_GB_3species.RDS") #courser resolution
#read in GB strata
#haddock contains all stratas used
Had_ras <- readRDS(file="TestScripts/Habitat_plots/Haddock/Had_Weighted_AdaptFalse_RASTER_res2.RDS")
#plot(Had_ras)
#translate habitat matrix back into raster
hab_ras <-raster(hab$hab$spp3)
extent(hab_ras) <- extent(Had_ras)
#plot(hab_ras)
for(iter in seq(length(list_all))){
print(iter)
temp <- matrix(data=0,nrow=length(list_all[[iter]][,1]),ncol=n_spp)
lat <- vector()
lon <- vector()
for(samp in seq(length(list_all[[iter]][,1]))){
#ADDING TRUE POPULATION
x = as.numeric(list_all[[iter]][samp,2]) #x in second column
y = as.numeric(list_all[[iter]][samp,3]) #y in third column
wk = as.numeric(list_all[[iter]][samp,11]) #week in 11th column
yr = as.numeric(list_all[[iter]][samp,7]) #year in 7th column
temp[samp,1] <- sum(result[[good_iter[iter]]]$pop_bios[[(wk+(52*(yr-1)))]][["spp1"]],na.rm=T) #YT is spp1
temp[samp,2] <- sum(result[[good_iter[iter]]]$pop_bios[[(wk+(52*(yr-1)))]][["spp2"]],na.rm=T) #Cod is spp2
temp[samp,3] <- sum(result[[good_iter[iter]]]$pop_bios[[(wk+(52*(yr-1)))]][["spp3"]],na.rm=T) #Had is spp3
#ADDING LAT LON LOCATIONS
rw <- as.numeric(list_all[[iter]][samp,"x"]) #x in col 2
cl <- as.numeric(list_all[[iter]][samp,"y"]) #y in col 3
lon[samp] <- xFromCol(hab_ras, col = cl)
lat[samp] <- yFromRow(hab_ras, row = rw)
}
temp <- cbind(temp,lat,lon)
colnames(temp) <- c("YT","Cod","Had","Latitude","Longitude")
list_all[[iter]] <- cbind(list_all[[iter]],temp)
colnames(list_all[[iter]]) <- c("station_no","x","y","stratum","day","tow","year","YT_samp","Cod_samp","Had_samp","week","Season","YT_pop","Cod_pop","Had_pop","Lat","Lon")
}
#SAVE INDIVIDUAL LIST_ALL AS THEY COME OUT SO DONT HAVE TO REDO THEM
#saveRDS(list_all,paste("list_all_more_",scenario,".RDS",sep=""))
#FIND MEAN VALUE BY SEASON USING ABOVE INFORMATION. USE MEAN OF TWO SURVEY WEEKS FOR EACH SEASON
season_wks <- list(c(13,14),c(37,38))
pop_by_season <- list()
for(iter in seq(length(list_all))){
for(s in short_names){
temp <- data.frame()
idx <- 1
for(yr in seq(3,22)){
for(season in seq(2)){
temp[idx,1] <- yr
temp[idx,2] <- season
#use values in given year for weeks in specified season. only use single strata because entire population summarized in each strata in above loop
temp[idx,3] <- mean(as.numeric(list_all[[iter]][((as.numeric(list_all[[iter]][,"year"]==yr)) & (as.numeric(list_all[[iter]][,"week"]) %in% season_wks[[season]]) & (as.numeric(list_all[[iter]][,"stratum"]==29)) ),paste(s,"_pop",sep="")]))
idx <- idx + 1
}
}
colnames(temp) <- c("year","season","biomass")
pop_by_season[[s]][[iter]] <- temp
}
}
pop_by_season[["YT"]] <- pop_by_season[["YT"]][[1]] #YT should be first in list_all
pop_by_season[["Cod"]] <- pop_by_season[["Cod"]][[2]] #Cod should be second in list_all
pop_by_season[["Had"]] <- pop_by_season[["Had"]][[3]] #Had should be third in list_all
##########################################################################################
#NOW WE NEED TO CREATE A STRATIFIED MEAN FROM EACH OF THESE SAMPLES
##########################################################################################
#choose some strata to exclude, if desired
#George's Bank Setup by species
#YT Cod Haddock
exclude <- list()
ifelse(exclude_strata==TRUE,
{exclude[["YT"]] <- c(13,14,15,17,18)
exclude[["Cod"]] <- c(23,24,25)
exclude[["Had"]] <- c(23,24,25,29,30)},
{exclude[["YT"]] <- c(0)
exclude[["Cod"]] <- c(0)
exclude[["Had"]] <- c(0)})
setwd(orig.dir)
#BELOW WILL TAKE A MINUTE
#there are #strata * #iterations * #samp_per_iter total samples
#I AM COPYING FROM CALC_SRS_INDEX_SURVEY_BENS, which was adapted from Liz's code to create below
library(tibble)
library(ggplot2)
library(plyr)
library(dplyr)
library(tidyr)
library(readr)
library(here)
#stop output from below using this option
options(dplyr.summarise.inform = FALSE)
#load file to calculate the stratified mean
source("TestScripts/Calc_strat_mean/fn_srs_survey_BENS.R")
#setup dimensions for each species- 1 for each strata
strat_mean_all <- vector("list",length(seq(n_spp)))
for(s in seq(n_spp)){
strat_mean_all[[s]] <- vector("list",length(list_all))
}
#go through each strata survey, iteration, sample
for(iter in seq(length(list_all))){
print(iter)
#if any NA columns make them zero
list_all[[iter]][is.na(list_all[[iter]])]=0
# calculate SRS estimates ====
for(s in seq(n_spp)){
#DEFINE INDIVIDUAL STRATUM AREAS
stratum <- sort(unique(surv_random$log.mat[,4]))
STRATUM_AREA <- na.omit(surv_random$cells_per_strata) # old way: rep(10000/nstrata,nstrata) #100x100 grid so each corner has area 2500
sv.area <- as_tibble(data.frame(stratum,STRATUM_AREA))
#remove stratum that species does not occupy
sv.area <- sv.area[(sv.area$stratum %in% strata_species[[short_names[s]]]),]#sv.area %>% slice(-exclude)
#remove strata to exclude from stratified mean calculation
sv.area <- sv.area[!(sv.area$stratum %in% exclude[[s]]),]#sv.area %>% slice(-exclude)
spp <- as_tibble(list_all[[iter]],header=T) #pull out entire survey matrix
##remove strata to exclude from stratified mean calculation
spp <- spp[(spp$stratum %in% strata_species[[short_names[s]]]),]
spp <- spp[!(spp$stratum %in% exclude[[s]]),]
spp$year <- as.numeric(spp$year)
# get total area of stock ====
spp.strata <- unique(spp$stratum)
spp.strata <- as.numeric(spp.strata)
spp.area <- sum(sv.area$STRATUM_AREA[sv.area$stratum %in% spp.strata]) #TOTAL AREA OF ALL STRATA
temp <- srs_survey(df=spp, sa=sv.area, str=NULL, ta=1, sppname = paste0(short_names[s],"_samp", sep="") ) # if strata=NULL, the function will use the unique strata set found in df
# View(temp)
strat_mean_all[[s]][[iter]] <- temp %>%
mutate(mean.yr.absolute=mean.yr*spp.area, sd.mean.yr.absolute=sd.mean.yr*spp.area,
CV.absolute=sd.mean.yr.absolute/mean.yr.absolute) # if strata=NULL, the function will use the unique strata set found in df
strat_mean_all[[s]][[iter]] <- data.matrix(strat_mean_all[[s]][[iter]])
}
colnames(strat_mean_all[[s]][[iter]]) <- c("year","mean.yr","var.mean.yr","sd.mean.yr","CV","season","mean.yr.absolute","sd.mean.yr.absolute","CV.absolute")
}
#initial scenario folder
dir.create( paste0(getwd(),"/VAST/",scenario)) #create folder to store upcoming subfolders
strat_mean_all[["YT"]] <- strat_mean_all[[1]][[1]] #YT should be first in list_all
strat_mean_all[["Cod"]] <- strat_mean_all[[2]][[2]] #Cod should be second in list_all
strat_mean_all[["Had"]] <- strat_mean_all[[3]][[3]] #Had should be third in list_all
ifelse(exclude_strata==TRUE, strat_ex <- "excludestrata", strat_ex <- "allstrata")
saveRDS(strat_mean_all,paste0(getwd(),"/VAST/",scenario,"/strat_mean_all_",scenario,"_",strat_ex,".RDS"))
#load VAST fit index approximation, measure error with true value, store
#individual strata limits
# strata.limits <- list()
# strata.limits[["YT"]] <- data.frame(Georges_Bank = c(1130, 1140, 1150, 1160, 1170, 1180, 1190, 1200, 1210)) #THESE ARE YTF STRATA
# strata.limits[["Cod"]] <- data.frame(Georges_Bank = c(1130, 1140, 1150, 1160, 1170, 1180, 1190, 1200, 1210, 1220, 1230, 1240, 1250)) #THESE ARE COD STRATA
# strata.limits[["Had"]] <- data.frame(Georges_Bank = c(1130, 1140, 1150, 1160, 1170, 1180, 1190, 1200, 1210, 1220, 1230, 1240, 1250, 1290, 1300)) #THESE ARE HAD STRATA
#What I tested for all of them, but they dont always work in each scenario
model_types <- list()
model_types[["YT"]] <- c("obsmodel5","obsmodel6")
model_types[["Cod"]] <- c("obsmodel1","obsmodel2","obsmodel5","obsmodel6")
model_types[["Had"]] <- c("obsmodel1","obsmodel2","obsmodel5","obsmodel6")
#Use these for IncPop_IncTemp
# model_types <- list()
# model_types[["YT"]] <- c("obsmodel5","obsmodel6")
# model_types[["Cod"]] <- c("obsmodel2","obsmodel5") #,"obsmodel6" didnt work
# model_types[["Had"]] <- c("obsmodel1")
# ifelse(exclude_strata==TRUE,
# {model_types[["YT"]] <- c("obsmodel5","obsmodel6")
# model_types[["Cod"]] <- c("obsmodel1","obsmodel2","obsmodel5") #,"obsmodel6" didnt work
# model_types[["Had"]] <- c("obsmodel1","obsmodel2","obsmodel5","obsmodel6")},
# {model_types[["YT"]] <- c("obsmodel1","obsmodel2","obsmodel3","obsmodel4","obsmodel5","obsmodel6")
# model_types[["Cod"]] <- c("obsmodel1","obsmodel2","obsmodel3","obsmodel4","obsmodel5","obsmodel6")
# model_types[["Had"]] <- c("obsmodel1","obsmodel2","obsmodel3","obsmodel4","obsmodel5","obsmodel6")}
# )
Model_settings <- list()
Model_AIC <- list()
SRS_data_all <- list()
SRS_data1 <- list()
VAST_Model_error <- list()
SRS_Model_error <- list()
VAST_fit <- list()
VAST_est <- list()
FC_settings <- list()
FC_settings[["YT"]][["spring"]] <- data.frame(row.names = model_types[["YT"]])
FC_settings[["YT"]][["fall"]] <- data.frame(row.names = model_types[["YT"]])
FC_settings[["Cod"]][["spring"]] <- data.frame(row.names = model_types[["Cod"]])
FC_settings[["Cod"]][["fall"]] <- data.frame(row.names = model_types[["Cod"]])
FC_settings[["Had"]][["spring"]] <- data.frame(row.names = model_types[["Had"]])
FC_settings[["Had"]][["fall"]] <- data.frame(row.names = model_types[["Had"]])
#for getting into correct subfolder
ifelse(exclude_strata==TRUE,
{str_dir <- "ExcludeStrata"},
{str_dir <- "AllStrata"})
#initial scenario folder
setwd( paste0(orig.dir,"/VAST/",scenario,sep="")) #create folder to store upcoming subfolders
for(s in short_names){
for(folder in model_types[[s]]){
print(folder)
for(sn in c("fall","spring")){
try(fit <- readRDS(paste0(getwd(),"/",s,"/",str_dir,cov_dir,"/",folder,"/",sn,"/fit_",sn,".RDS")),silent=TRUE)
#set FC settings as N and then override them, if possible
FC_settings[[s]][[sn]][folder,1] <- "N"
FC_settings[[s]][[sn]][folder,2] <- "N"
FC_settings[[s]][[sn]][folder,3] <- "N"
FC_settings[[s]][[sn]][folder,4] <- "N"
try(FC_settings[[s]][[sn]][folder,1] <- fit$settings$FieldConfig[[1]],silent=TRUE)
try( FC_settings[[s]][[sn]][folder,2] <- fit$settings$FieldConfig[[2]],silent=TRUE)
try( FC_settings[[s]][[sn]][folder,3] <- fit$settings$FieldConfig[[3]],silent=TRUE)
try(FC_settings[[s]][[sn]][folder,4] <- fit$settings$FieldConfig[[4]],silent=TRUE)
try(VAST_fit[[s]][[folder]][[sn]] <- read.csv(paste0(getwd(),"/",s,"/",str_dir,cov_dir,"/",folder,"/",sn,"/Index.csv"), header=T),silent=TRUE)
#pull out strat mean calc
SRS_data_all[[s]][[folder]][["spring"]] <- strat_mean_all[[s]][strat_mean_all[[s]][,"season"]==1,]
SRS_data_all[[s]][[folder]][["fall"]] <- strat_mean_all[[s]][strat_mean_all[[s]][,"season"]==2,]
ifelse(sn == "spring",
#add year & season to these
{Year <- seq(years_cut+1,years_sim)
season <- rep(1,years_sim-years_cut)},
{Year <- seq(years_cut+1,years_sim)
season <- rep(2,years_sim-years_cut)})
try(VAST_est[[s]][[folder]][[sn]] <- cbind(VAST_fit[[s]][[folder]][[sn]],Year,season),silent=TRUE)
SRS_data1[[s]][[folder]][[sn]] <- SRS_data_all[[s]][[folder]][[sn]][,c("mean.yr.absolute","year","season","sd.mean.yr.absolute")]
try(Model_AIC[[s]][[sn]][[folder]] <- fit$parameter_estimates$AIC,silent=TRUE)
try(Model_settings[[s]][[sn]][[folder]] <- read.delim(paste0(getwd(),"/",s,"/",str_dir,cov_dir,"/",folder,"/",sn,"/settings.txt",sep="")),silent=TRUE)
try(remove(fit),silent=TRUE)
}
}
colnames(FC_settings[[s]][["spring"]]) <- c("Omega1","Epsilon1","Omega2","Epsilon2")
colnames(FC_settings[[s]][["fall"]]) <- c("Omega1","Epsilon1","Omega2","Epsilon2")
}
pdf(file=paste(getwd(),"/",scenario,"_ErrorPlot_",str_dir,cov_dir,".pdf",sep=""))
year_min <- 2 #in case you dont want to plot all of the years
VAST_data <- list()
SRS_data <- list()
Obsmodel_plot <- list()
#plot stratified calculation and population estimate on same plot
#first make model output have 2 seasons to match the stratified mean calcs
for(s in short_names){ #LIST_ALL WILL BE LENGTH 3 FROM ABOVE
for(folder in model_types[[s]]){
#MODEL VALUES
model_spring = pop_by_season[[s]][pop_by_season[[s]]$season==1,"biomass"]
model_fall = pop_by_season[[s]][pop_by_season[[s]]$season==2,"biomass"]
# 2NORM
# #calculate SPRING VAST error from each iteration
# VAST_Model_error[[s]][[folder]][["spring"]] <- norm(model_spring- VAST_est[[s]][[folder]][["spring"]][,"Estimate"] , type="2") / norm(model_spring , type ="2")
#
# #calculate FALL VAST error from each iteration
# VAST_Model_error[[s]][[folder]][["fall"]] <- norm(model_fall- VAST_est[[s]][[folder]][["fall"]][,"Estimate"] , type="2") / norm(model_fall , type ="2")
print(s)
print(folder)
#ABSOLUTE SUM
#calculate SPRING VAST error from each iteration
#set as 99 and then override if possible
VAST_Model_error[[s]][[folder]][["spring"]] <- 99
try(VAST_Model_error[[s]][[folder]][["spring"]] <- sum(abs(model_spring- VAST_est[[s]][[folder]][["spring"]][,"Estimate"] )) / sum(abs(model_spring )),silent=TRUE)
VAST_Model_error[[s]][[folder]][["fall"]] <- 99
#calculate FALL VAST error from each iteration
try(VAST_Model_error[[s]][[folder]][["fall"]] <- sum(abs(model_fall- VAST_est[[s]][[folder]][["fall"]][,"Estimate"] )) / sum(abs(model_fall )),silent=TRUE)
#calculate SPRING SRS error from each iteration
SRS_Model_error[[s]][[folder]][["spring"]] <- sum(abs(model_spring- SRS_data1[[s]][[folder]][["spring"]][,"mean.yr.absolute"] )) / sum(abs(model_spring ))
#calculate FALL SRS error from each iteration
SRS_Model_error[[s]][[folder]][["fall"]] <- sum(abs(model_fall- SRS_data1[[s]][[folder]][["fall"]][,"mean.yr.absolute"] )) / sum(abs(model_fall ))
#store VAST stuff to plot later
#first load a blank version and override if possible
VAST_data[[s]][[folder]] <- readRDS(file = paste0(orig.dir,"/VAST/zero_VAST_est.RDS",sep="") )
#for decpop_contemp
#if(((!((s=="Cod"&folder=="obsmodel6")|(s=="Had"&folder=="obsmodel6"))))){ try(VAST_data[[s]][[folder]] <- rbind(VAST_est[[s]][[folder]][["spring"]],VAST_est[[s]][[folder]][["fall"]]),silent=TRUE)}
if(length(VAST_est[[s]][[folder]][["fall"]][1,])>2){try(VAST_data[[s]][[folder]] <- rbind(VAST_est[[s]][[folder]][["spring"]],VAST_est[[s]][[folder]][["fall"]]),silent=TRUE)}
SRS_data[[s]][[folder]] <- rbind(SRS_data1[[s]][[folder]][["spring"]],SRS_data1[[s]][[folder]][["fall"]])
long_names <- c("Yellowtail Flounder", "Atlantic Cod", "Haddock")
#NEW WAY PLOTTING 3 TOGETHER ON SAME PAGE
#field config settings for plotting
FC_fall = c(FC_settings[[s]]$fall[folder,1],FC_settings[[s]]$fall[folder,2],FC_settings[[s]]$fall[folder,3],FC_settings[[s]]$fall[folder,4])
FC_spring = c(FC_settings[[s]]$spring[folder,1],FC_settings[[s]]$spring[folder,2],FC_settings[[s]]$spring[folder,3],FC_settings[[s]]$spring[folder,4])
#store each obsmodel plot
Obsmodel_plot[[s]][[folder]] <- ggplot() +
#this way plots data by season
geom_point(data = subset(as.data.frame(pop_by_season[[s]]),year>=year_min), aes(x=as.numeric(year),y=biomass, group = season, color = "Model"),size=2) +
geom_line(data = subset(as.data.frame(pop_by_season[[s]]),year>=year_min), aes(x=as.numeric(year),y=biomass, group =season, color = "Model"),size=1) +
#plot VAST estimate
geom_errorbar(data=subset(VAST_data[[s]][[folder]],Year>=year_min),aes(x=Year,y=Estimate,group=season,ymin=Estimate-(1.96*Std..Error.for.Estimate), ymax=Estimate+(1.96*Std..Error.for.Estimate), color = "VAST Estimate"),width=.3) +
#geom_linerange(data=subset(VAST_data[[s]][[folder]],Year>=year_min),aes(x=Year,y=Estimate,group=season,ymin=Estimate-(1.96*Std..Error.for.Estimate), ymax=Estimate+(1.96*Std..Error.for.Estimate), color = "VAST Estimate")) +
geom_point(data=subset(VAST_data[[s]][[folder]],Year>=year_min),aes(x=Year,y=Estimate,group=season, color = "VAST Estimate"))+
geom_line(data=subset(VAST_data[[s]][[folder]],Year>=year_min),aes(x=Year,y=Estimate,group=season, color = "VAST Estimate"))+
#plot stratified calculation data
geom_errorbar(data=as.data.frame(SRS_data[[s]][[folder]]),aes(x=year,y=mean.yr.absolute,group=season,ymin=mean.yr.absolute-(1.96*sd.mean.yr.absolute), ymax=mean.yr.absolute+(1.96*sd.mean.yr.absolute), color = "Stratified Mean"),width=.3) +
# geom_linerange(data=as.data.frame(SRS_data[[s]][[folder]]),aes(x=year,y=mean.yr.absolute,group=season,ymin=mean.yr.absolute-(1.96*sd.mean.yr.absolute), ymax=mean.yr.absolute+(1.96*sd.mean.yr.absolute), color = "Stratified Mean")) +
geom_point(data=as.data.frame(SRS_data[[s]][[folder]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
geom_line(data=as.data.frame(SRS_data[[s]][[folder]]),aes(x=year,y=mean.yr.absolute,group=season, color = "Stratified Mean"))+
facet_wrap(~ season, ncol =1) +
# labs(x="year",y="Biomass", title = paste(folder," SeV=",round(VAST_Model_error[[s]][[folder]][["spring"]],digits=2),
# " FC=", toString(FC_spring),
# " SeSM=",round(SRS_Model_error[[s]][[folder]][["spring"]],digits=2),
# " FeV=",round(VAST_Model_error[[s]][[folder]][["fall"]],digits=2),
# " FC=", toString(FC_fall),
# " FeSM=",round(SRS_Model_error[[s]][[folder]][["fall"]],digits=2),sep=""), color ="" )
labs(x="year",y="Biomass", title = paste(s," ",folder," SeV=",round(VAST_Model_error[[s]][[folder]][["spring"]],digits=2),
" SeSM=",round(SRS_Model_error[[s]][[folder]][["spring"]],digits=2),
" FeV=",round(VAST_Model_error[[s]][[folder]][["fall"]],digits=2),
" FeSM=",round(SRS_Model_error[[s]][[folder]][["fall"]],digits=2),sep=""), color ="" )
#one plot per page
print(Obsmodel_plot[[s]][[folder]])
# for more than one plot per page
# gridExtra::grid.arrange(Obsmodel_plot[[1]],Obsmodel_plot[[2]],Obsmodel_plot[[3]],nrow=3)
# gridExtra::grid.arrange(Obsmodel_plot[[4]],Obsmodel_plot[[5]],Obsmodel_plot[[6]],nrow=3)
}
}
dev.off()
BELOW HAS NOT YET BEEN UPDATED TO BE GENERALIZED FOR ALL SPECIES. ONLY APPLIES TO YTF
##########################################################################################
#Next plot scatterplot of errors
##########################################################################################
#first create data from for each
#1- stratified mean data
df_SRS_spring <- tibble(iter = rep(1:length(list_all),n_spp),
error = c(SRS_error_spring[[1]], SRS_error_spring[[2]], SRS_error_spring[[3]]),
species = c(rep("YTF",length(list_all)),rep("Cod",length(list_all)),rep("Had",length(list_all))),
season = rep(rep("spring",length(list_all)),n_spp),
Model = rep(rep("Strat. Mean",length(list_all)),n_spp),
)
df_SRS_fall <- tibble(iter = rep(1:length(list_all),n_spp),
error = c(SRS_error_fall[[1]], SRS_error_fall[[2]], SRS_error_fall[[3]]),
species = c(rep("YTF",length(list_all)),rep("Cod",length(list_all)),rep("Had",length(list_all))),
season = rep(rep("fall",length(list_all)),n_spp),
Model = rep(rep("Strat. Mean",length(list_all)),n_spp),
)
df_SRS <- rbind(as.data.frame(df_SRS_fall),as.data.frame(df_SRS_spring))
#create data frame containing mean values for each group
means_SRS <- ddply(df_SRS, .(species,season), summarise, mean = mean(as.numeric(unlist(error)),na.rm=T), Model = "Strat. Mean")
#2- VAST data
df_VAST_spring <- tibble(iter = rep(1:length(list_all),n_spp),
error = c(VAST_error_spring[[1]], VAST_error_spring[[2]], VAST_error_spring[[3]]),
species = c(rep("YTF",length(list_all)),rep("Cod",length(list_all)),rep("Had",length(list_all))),
season = rep(rep("spring",length(list_all)),n_spp),
Model = rep(rep("VAST",length(list_all)),n_spp),
)
df_VAST_fall <- tibble(iter = rep(1:length(list_all),n_spp),
error = c(VAST_error_fall[[1]], VAST_error_fall[[2]], VAST_error_fall[[3]]),
species = c(rep("YTF",length(list_all)),rep("Cod",length(list_all)),rep("Had",length(list_all))),
season = rep(rep("fall",length(list_all)),n_spp),
Model = rep(rep("VAST",length(list_all)),n_spp),
)
df_VAST <- rbind(df_VAST_fall,df_VAST_spring)
#create data frame containing mean values for each group
means_VAST <- ddply(df_VAST, .(species,season), summarise, mean = mean(as.numeric(unlist(error))), Model = "VAST")
#combine both of previous data into single object for plotting
df <- rbind(df_VAST,df_SRS)
means <- rbind(means_SRS,means_VAST)
#Error scatterplots
#1) to plot a single scenario, run just cc below and print(cc)
#2) to plot two scenarios on top of each other, store the first as cc and then run code below doing cc +
library(ggplot2)
# #SRS scatterplot alone
# SRS_scat <-ggplot(data=df_SRS,
# aes(x=iter,y=as.numeric(error),color=Model)) +
# geom_point()+
# ylim(0,1)+
# facet_grid(season ~ species)+
# geom_hline(aes(yintercept = mean, color = Model), data = means_SRS)
#
# print(SRS_scat)
#
# #VAST scatterplot alone
# VAST_scat <-ggplot(data=df_VAST,
# aes(x=iter,y=as.numeric(error),color=Model)) +
# geom_point()+
# ylim(0,1)+
# facet_grid(season ~ species)+
# geom_hline(aes(yintercept = mean, color = Model), data = means_VAST)
#
# print(VAST_scat)
#both scatterplots together
both_scat <-ggplot(data=df,
aes(x=iter,y=as.numeric(unlist(error)),color=Model)) +
geom_point()+
ylim(0,1)+
facet_grid(season ~ species)+
geom_hline(aes(yintercept = mean, color = Model), data = means)
print(both_scat)
#
# ggsave(filename = paste("Results/GB_error_plots/Individussssal_SRS_",scenario,".pdf",sep=""),
# plot = last_plot())
#
#run this second to plot on top of each other
cc+ geom_point(data=df,color="red",
aes(x=iter,y=as.numeric(error)))+
facet_grid(season ~ species) +
geom_hline(aes(yintercept = mean), data = means, color = "red")
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.